QA 14. Machine-Learning Methods

Learning Objectives

1) Discuss the philosophical and practical differences between machine-learning techniques and classical econometrics.

2) Explain the differences among the training, validation, and test data sub-samples, and how each is used.

3) Understand the differences between and consequences of underfitting and overfitting, and propose potential remedies for each.

4) Use principal components analysis to reduce the dimensionality of a set of features.

5) Describe how the K-means algorithm separates a sample into clusters.

6) Be aware of natural language processing and how it is used.

7) Differentiate among unsupervised, supervised, and reinforcement learning models.

8) Explain how reinforcement learning operates and how it is used in decision-making.